Crate rusty_planner

This is documentation for the rusty_planner crate.

AI Planning & Reasoning library for Rust

This is a library written in Rust containing Artificial Intelligence algorithms - in particular those from the field of planning & reasoning. The goal is to support the following algorithms in the long run:

• Generic Planning Algorithms
  • M. Likhachev, D. Ferguson, G. Gordon, A. Stentz, and S. Thrun, Anytime dynamic A*: an anytime, replanning algorithm. Fifteenth International Conference on International Conference on Automated Planning and Scheduling (ICAPS’05), 2005, https://dl.acm.org/doi/10.5555/3037062.3037096
  • S. Koenig and M. Likhachev, D*lite. Eighteenth national conference on Artificial intelligence, 2002, https://dl.acm.org/doi/10.5555/777092.777167
  • D. Knuth, Dancing Links, Millennial Perspectives in Computer Science., https://arxiv.org/pdf/cs/0011047.pdf
  • S. Gelly, Y. Wang, R. Munos, and O. Teytaud Modification of UCT with Patterns in Monte-Carlo Go. Technical report, INRIA, 2006, http://hal.inria.fr/docs/00/12/15/16/PDF/RR-6062.pdf
  • M. Zweben, E. Davis, B. Daun and M. J. Deale, Scheduling and rescheduling with iterative repair. IEEE Transactions on Systems, Man, and Cybernetics, 1993, https://ieeexplore.ieee.org/document/257756
• Multi-Agent Planning - Coordination, Negotiation/Bidding, Coalition Formation:
  • T. Sandholm, K. Larson, M. Andersson, O. Shehory, and F. Tohmé, Coalition structure generation with worst case guarantees. Artif. Intell. 111, 1999, https://doi.org/10.1016/S0004-3702(99)00036-3
  • David Silver, Cooperative pathfinding. First AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’05), 2005, https://dl.acm.org/doi/10.5555/3022473.3022494
  • R. Nissim and R. Brafman, Distributed Heuristic Forward Search for Multi-Agent Systems. arXiv, 2013. https://arxiv.org/abs/1306.5858
  • O. Shehory and S. Kraus, Task allocation via coalition formation among autonomous agents. 14th international joint conference on Artificial intelligence - Volume 1 (IJCAI’95), 1995, https://dl.acm.org/doi/10.5555/1625855.1625941

Note: This library is still work in progress - and mostly for me to get familiar with Rust. Use at your own risk; refactorings & bigger changes might still happen.

To make use of the distributed decision-making algorithms, please ensure to enable the multi_agent feature.

Motivation

While Deep Learning algorithms are currently a hot topic, it is also necessary to have planning & reasoning capabilities to build truly autonomous systems. Be it for robots moving around, ways to automatically plan how ships should berth, in which order airplanes should land on airports, etc.. There are countless examples of how these algorithms can help solve real world problems & deliver efficient solutions. Recommended reads include Artificial Intelligence: A Modern Approach by Stuart Russell & Peter Norvig; Furthermore NASA’s JPL Artificial Intelligence group has done tons of research in this area - especially their methods of controlling autonomous space probes using planners & execution components are interesting; Imagine how cool it would be if those concept would be embedded in future control planes…

Modules

• any_dyn_astar
  Module implementing the Anytime Dynamic A* algorithm.
• dstar_lite
  Module implementing the D* lite algorithm.
• iterative_repair
  Module implementing an iterative repair algorithm.
• mcts
  Module implementing a UCT style Monte-Carlo Tree Search algorithm.
• planner
  Module with some generic traits used by various algorithms.